Analysis of band Selection algorithms for endmember extraction in hyperspectral Images

Abstract

This paper presents a novel approach of band selection for dimensionality reduction in Hyperspectral images (HSI). There are several methods of dimensionality reduction which can be further categorized into two groups; feature extraction and feature or band selection. Due to transformation in feature extraction, the critical information may have been distorted. Hence feature selection is preferable for dimensionality reduction because it preserves the relevant original information. Despite many algorithms exist for dimensionality reduction; it is even now a challenging task of selecting informative bands from the large volume data. The number of bands is estimated with the concept of Virtual Dimensionality (VD), because it provides reliable estimate. Bands are selected from hyperspectral images using Exemplar Based Band Selection (EBBS). End members are extracted from the selected bands using Simplex Growing Algorithm(SGA). The performance of EBBS is compared with the existing band selection techniques such as Constrained Band Selection (CBS) and Similarity Based Band Selection (SBBS) using the spectral angle distance as a measure. Keywords: Hyperspectral images, Virtual Dimensionality, Simplex Growing Algorithm, Exemplar based band selection, Spectral angle distance.